Chromosomal mutations conferring antibiotic resistance in Escherichia coli will be used to study the problem of increasing levels of antibiotic-resistant bacteria, and as a tool to increase our understanding of the process of adaptive evolution in bacteria. The overall goals of the study are (i) to quantify the extent to which antibiotic resistance mutations impair the fitness of bacteria, and how these costs of resistance are modified by natural selection and (ii) to elucidate the genetic and physiological bases of adaptations to the fitness costs of resistance mutations. To achieve these goals, the proposed research will focus on experimental populations of E. coli with well-characterized mutations conferring resistance to streptomycin (rpsL) and rifampin (rpoB). These mutations reduce bacterial fitness by impairing mRNA translation and DNA transcription, respectively. These resistant strains will be maintained in the absence of antibiotics and monitored for the evolution of higher fitness mutants. The latter could be sensitive revertants at the rpsL or rpoB loci or, as our present results suggest, strains carrying second site compensatory mutations that increase fitness while maintaining resistance. The genetic, molecular and physiological basis of these adaptations will be analyzed. Additionally, mathematical models of adaptive evolution in steady-state populations of bacteria and bacterial populations subject to periodic bottlenecks will be developed, and their properties analyzed independent estimates of the parameters of these models and experiments under controlled conditions will allow for direct tests of the predictive value of this theory. In addition to contributing to our understanding of the population dynamics and the genetic, and physiological basis of adaptive evolution in bacterial populations, these studies have direct implications for a human health problem, the spread of antibiotic resistant pathogens. The aims of this study, to investigate costs of antibiotic resistance and the nature of adaptation to those costs, are essential for predictions about rates of antibiotic use and the epidemiology of antibiotic resistance, and for the design of multiple antibiotic use protocols that minimize the problems of resistance.